Metadata Overview

“Metadata is structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource. Metadata is often called data about data or information about information.” It ensures that the context for how your data was created, analysed and stored, is clear, detailed and therefore, reproducible. (National Information Standards Organization, 2004)

Good metadata enables you to understand, use, and share your own data now and in the future, and helps other researchers discover, access, use, repurpose, and cite your data in the long-term. It also facilitates long-term archival preservation of the data.

Experimental Metadata: Information about the experimental conditions (e.g. assay type, time points), the experimental protocol, and the equipment used to generate the data.

Analytical Metadata: Information about data analysis methods including software name and version, quality control parameters, and output file type details.

Dataset Level Metadata: Information about the objectives of the research project, participating investigators, relevent publications, and funding sources.

When to Record MetadataMany fields within the biomedical science community are developing standards for what metadata to collect across different data types. Whenever possible, it is best to consult community standards before you begin collecting research data. It is easiest and most efficient to record metadata during the research process, while the data still are active. This also ensures that the metadata recorded are complete and accurate.

It’s likely that your metadata will come from several sources during your research.

Most other metadata are manually collected. Consider using an exisitng schema or template to make the process easier.

How to Record Metadata

Experimental and data analysis metadata should be stored alongside your research data such as in lab notebooks, databases, or in .txt“README”files. Where possible, employ one or multiple established metadata schemas that are widely used within your discipline. If you are storing your data in a repository, you also must comply with its metadata requirements. Below are some examples of discipline-specific metadata schemas and links to more extensive lists of metadata schemas.